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Minimizing Tension in Teams

Published: 06 November 2017 Publication History

Abstract

In large organizations (e.g., companies, universities, etc.) individual experts with different work habits are asked to work together in order to complete projects or tasks. Oftentimes, the differences in the inherent work habits of these experts causes tension among them, which can prove detrimental for the organization's performance and functioning. The question we consider in this paper is the following: "can this tension be reduced by providing incentives to individuals to change their work habits?" We formalize this question in the definition of the k- AlterHabit problem. To the best of our knowledge we are the first to define this problem and analyze its properties. Although we show that k- AlterHabit is NP-hard, we devise polynomial-time algorithms for solving it in practice. Our algorithms are based on interesting connections that we draw between our problem and other combinatorial problems. Our experimental results demonstrate both the efficiency and the efficacy of our algorithmic techniques on a collection of real data.

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cover image ACM Conferences
CIKM '17: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management
November 2017
2604 pages
ISBN:9781450349185
DOI:10.1145/3132847
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 06 November 2017

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Author Tags

  1. algorithms
  2. collaboration networks
  3. experimentation
  4. teams
  5. theory

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CIKM '17 Paper Acceptance Rate 171 of 855 submissions, 20%;
Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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